Andreas Holzinger“Science is to test crazy ideas – Engineering is to put these into Business”

Assoc.Prof.Dr. Andreas HOLZINGER

PhD, MSc, MPh, BEng, CEng, DipEd, MBCS

MAKE-Video|MAKE-Journal|CD-MAKE|Research 5p|Teaching 5p|CV 3p

Holzinger Group HCI-KDD
Institute for Medical Informatics, Statistics & Documentation
Medical University Graz,  and
Institute of Interactive Systems and Data Science
Graz University of Technology

Andreas Holzinger is doing with his group theoretical, algorithmical, and experimental studies to help to understand intelligence. He supports the international research community to answer a grand question: How can we perform a task by exploiting knowledge, extracted during solving previous tasks? Contributions to solve this problem would have impact to Artificial Intelligence generally, and Machine Learning specifically, to develop software which learns from experience similar as humans do.

Andreas Holzinger is lead of the Holzinger Group, HCI-KDD, Institute for Medical Informatics/Statistics at the Medical University Graz, and Associate Professor of Applied Computer Science at the Faculty of Computer Science and Biomedical Engineering at Graz University of Technology. He serves as consultant for the Canadian, US, UK, Swiss, French, Italian and Dutch governments, for the German Excellence Initiative, and as national expert in the European Commission. Andreas obtained a Ph.D. in Cognitive Science from Graz University in 1998 and his Habilitation (second Ph.D.) in Computer Science from Graz University of Technology in 2003. Andreas was Visiting Professor for Machine Learning & Knowledge Extraction in Verona, RWTH Aachen, University College London and Middlesex University London. Since 2016 Andreas is Visiting Professor for Machine Learning in Health Informatics at the Faculty of Informatics at Vienna University of Technology. He founded the Expert Network HCI-KDD to foster a synergistic combination of methodologies of two areas that offer ideal conditions toward unraveling problems in understanding intelligence: Human-Computer Interaction (HCI) & Knowledge Discovery/Data Mining (KDD), with the goal of supporting human intelligence with artificial intelligence. Andreas is Associate Editor of Knowledge and Information Systems (KAIS), Section Editor of BMC Medical Informatics and Decision Making (MIDM), and Editor-in-Chief of Machine Learning & Knowledge Extraction (MAKE). He is organizer of the IFIP Cross-Domain Conference “Machine Learning & Knowledge Extraction (CD-MAKE)” and member of IFIP WG 12.9 Computational Intelligence, the ACM, IEEE, GI, the Austrian Computer Science and the Association for the Advancement of Artificial Intelligence (AAAI). Since 2003 Andreas has participated in leading positions in 30+ R&D multi-national projects, budget 4+ MEUR, 300+ publications, 8500+ citations, h-Index = 42.

Technical Area: Machine Learning, Knowledge Extraction
Application Area: Health Informatics

More information about the Holzinger Group: http://hci-kdd.org/

The goal of the Holzinger Group is to develop software which can learn from data to extract knowledge and improve with experience over time. However, the application of such automatic machine learning (aML) algorithms in complex domains (e.g. Health) seems elusive in the near future, and a good example are Gaussian processes, where aML (e.g. standard kernel machines) struggle on function extrapolation problems which are trivial for human learners. Consequently, interactive machine learning (iML) with a human-in-the-loop, thereby making use of human cognitive abilities, can be of particular interest to solve problems, where learning algorithms suffer due to insufficient training samples, where we deal with complex data and/or rare events or computationally hard problems. A “doctor-in-the-loop” can help, and human expertise and long-term experience can assist in solving problems which otherwise would remain NP-hard. However, successful MAchine learning & Knowledge Extraction (MAKE) for health informatics requires a deep understanding of the data ecosystem and a concerted effort cross-domain of 7 research topics: 1) data integration, 2) learning algorithms, 3) visualization, 4) privacy, data protection, safety and security, 5) graphs, 6) topology, and 7) entropy. Please read the inaugural paper doi:10.3390/make1010001